The present disclosure relates generally to the field of indoor positioning and location-based information systems, and more specifically to systems and methods for providing an indication of position and position confidence near a structure periphery, such as when located proximate an exterior wall of a structure, when transitioning from an indoor location (i.e., first positioning system) to an outdoor location (i.e., second positioning system), and so on.
Typical location-based information systems depend on the global positioning systems (GPS) to determine a geographic location and a remote database that contains information associated with a particular geographic location. GPS receivers generally rely on navigation signals broadcasted by satellites orbiting the Earth. Such receivers require an essentially unobstructed line of sight to the satellites in order to provide reliable location information. Thus, GPS is typically used to establish locations in outdoor environments only and may not be suitable to indoor locations. Even in outdoor locations, the presence of tall buildings, and other obstructions in the line of sight can preclude GPS positioning. Indoor positioning systems (IPS) have been developed for use within indoor environments, such as office buildings. Such systems generally use various wireless transmissions, for example, infrared (IR) or ultrasound signals, for location and tracking purposes. Devices have also been configured to utilize other data sources, such as WiFi signals, Bluetooth, on-board compass and accelerometer (and/or gyroscope, magnometer, etc.) data, as well as existing floor plans and databases of measurements. Thus, geo-positioning devices are often configured to switch operation between different geo-positioning systems.
Indoor geo-positioning systems have a number of known limitations. For example, many such systems require special sensor/transceiver devices and infrastructure to be deployed with the interior spaces of buildings. Further, many existing systems fail to provide accurate position determinations due to the inherent interference limitations that occur within indoor spaces, and fail to provide any device orientation determinations. Further still, many existing systems require significant power consumption on the part of the mobile device. (For example, a system may require the mobile device to enable its GPS function or continually transmit a signal in order to determine its position.) Still further, certain systems require map or measurement data where none exist (e.g., a building has not been mapped or measured, nor its floor plan uploaded).
In one approach, a map of signal features of a structure is created, such as a grid of points, each point including a list of WiFi access points accessible at that point and the signal strength of each at that point. When determining location within the structure, a mobile device, such as computer-enabled mobile telephone (so-called smart phone), evaluates accessible WiFi access points and signal strengths wherever the device currently is compares that the map. A cluster of “particles” is created, each particle being a possible location of the mobile device. Each particle has associated with it a degree of confidence that the mobile device is at that point. The mean location of the clustered particles is then determined. The particle closest to the mean location is then selected as the location of the client device.
The location of the client device is often indicated by a dot displayed on a map, indicating position. However, it is difficult to accurately determine location at the scale of an indoor location (meter or less), particularly when GPS positioning systems are unavailable. Therefore, the indicated mobile device location is actually an indication of the greatest likelihood of position. In certain applications, a circle with the location dot as its center represents the overall degree of confidence that the indicated location is the current mobile device location. The smaller the radius of the circle, the higher the probability that the client device is located at the indicated dot. Thus, the radius of the circle is related to the confidence level in the indicated client device location. For this reason, the circle is referred to as a confidence circle.
However, when a client device is proximate a boundary between two different positioning systems, the device may experience difficulty determining which system to use for a current position determination and display. For example, when a client device is near the perimeter of a structure, the device may receive positioning data from a relatively higher-precisions indoor system (e.g., WiFi) as well as a relatively lower-precision outdoor system (e.g., cell tower triangulation). This may result in the device arbitrarily switching from one system to another—in certain cases showing the device location jumping back and forth between first and second positions (e.g., the dot jumping indoors, then outdoors). For many reasons, the first and second positions may be surprisingly far from one another. In addition, since each positioning system has its own associated accuracy and hence confidence circle, the radius of the confidence circle may vary when switching between systems in this way. Again, for many reasons the radius of the confidence circles at the first and second positions may vary greatly. Therefore, there remains a desire for improvements in the indication of location and confidence the system has in the location (i.e., location of the client device).